Electrocardiogram feature extraction method, device, system, equipment and classification method based on deep learning algorithm

Abstract

The invention discloses an electrocardiogram feature extraction method, device, system, equipment and classification method based on a deep learning algorithm. The electrocardiogram feature extractionmethod based on the deep learning algorithm comprises the following steps: randomly intercepting a section of continuous electrocardiogram signal from a twelve-lead electrocardiogram to be processed,wherein the electrocardiogram signal at least comprises two cardiac cycles; and inputting the intercepted electrocardiogram signal into a feature extraction model in a picture form, and performing extraction to obtain electrocardiogram signal features, wherein the feature extraction model is obtained by training a ResNet model or an Inception model or an Inception-ResNet model. According to the electrocardiogram feature extraction method, device, system, equipment and classification method based on the deep learning algorithm provided by the invention, the incompleteness caused by artificialdesign features is reduced, and the accuracy and diversity of electrocardiogram feature extraction based on the deep learning algorithm are improved.

Description

technical field [0001] The invention relates to the technical field of electrocardiogram signal processing, in particular to a method, device, system, equipment and classification method for extracting electrocardiographic features based on deep learning algorithms. Background technique [0002] ECG detection is currently the most important means of detecting and diagnosing heart diseases. Human electrocardiograph (ECG) is the comprehensive performance of the heart's electrical activity on the body surface. By extracting the characteristic information of the ECG, the physiological condition. [0003] Taking arrhythmia as an example, the current arrhythmia analysis mainly uses waveform analysis method and template matching method. The waveform analysis method first obtains the characteristic waveform parameters, such as the amplitude, time length, rise / fall time, waveform interval, etc. of the characteristic waveform. These waveform parameters are compared with the judgment ...

AI Technical Summary

Problems solved by technology

However, the template matching method can only make effective judgments when the R wave shape of the detected person is quite different from the template, and cannot effectively judge the arrhythmia waveforms with insignificant differences.

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